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Wheat spike and spikelet detection and counting from high-resolution digital imagery using YOLO with Oriented Bounding Boxes.

Scientific reports - 15 Jun 2026

Ghimire Hillson, Maimaitijiang Maitiniyazi, Thapa Subash, Kaushal Swas, Koupal Dante Jerome, Billah Mohammad Maruf, Poudel Kushal, Sehgal Shaurya, Singh Mandeep, Subedi Santosh, Ur Rehman Janjua Ubaid, Sehgal Sunish Kumar

Abstract excerpt

In-season estimation of wheat grain yield potential is critical for crop management and advancing breeding efforts. Spike and spikelet counts serve as key indicators directly linked to yield potential, yet their assessment still relies on manual counting which is both labor-intensive and error-prone. High-resolution digital (RGB) imagery combined with deep learning-based object detection methods has substantially...

Read the complete abstract on PubMed

Topics

Triticum
Detection Algorithms
Image Processing, Computer-Assisted
Phenotype
Deep Learning

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Edible Grain

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Identifiers

PMID: 42297952
DOI: 10.1038/s41598-026-55761-w
PMCID: PMC13529697

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